For the complete documentation index, see llms.txt. This page is also available as Markdown.

AI Models

Agents in JetAdmin can use different AI models depending on the task, performance needs, and cost considerations.

Available Models

Anthropic (Claude)

OpenAI

Google (Gemini)

Self-hosted

Claude 4.6 Opus

GPT 5.x (full)

Gemini 3.1 Pro

Llama

Claude 4.5 Opus

GPT 4.x (full)

Gemini 2.5 Pro

Mistral

Claude 4.1 Opus

GPT Mini

Gemini 3.1 Flash

Qwen

Claude 4.5 Sonnet

GPT Nano

Gemini 2.5 Flash

DeepSeek

Claude 4 Sonnet

Codex

Claude 4.5 Haiku

Understanding Model Trade-offs

Faster models are more cost-efficient and ideal for simple, high-volume tasks. More advanced models provide better reasoning, accuracy, and context handling, but consume more credits.

Best for fast responses and high-volume, low-cost tasks.

Model

Used For

Claude 4.5 Haiku

Quick responses, simple automation, chat assistants

Gemini Flash (2.5–3.1)

Lightweight tasks, fast lookups, basic workflows

OpenAI Nano

Short prompts, formatting, simple text handling

Llama (small)

Basic self-hosted tasks with minimal complexity

Balanced performance for most business use cases.

Model

Used For

Claude 4.5 Sonnet

General-purpose agents, multi-step tasks

Gemini Pro (2.5–3.1)

Data analysis, structured workflows

OpenAI Mini

Reliable automation, moderate reasoning tasks

Mistral / Qwen

Mid-level self-hosted workloads and processing

Best for complex reasoning, critical tasks, and high accuracy.

Model

Used For

Claude 4.1 / 4.5 / 4.6 Opus

Deep analysis, complex decision-making

OpenAI 5.x / 4.x (full models, Codex)

Advanced logic, coding, multi-step reasoning

Gemini Pro (latest versions)

Large context tasks, high-accuracy outputs

DeepSeek / large Llama

Advanced self-hosted reasoning and heavy workloads

Choosing Models & Managing Costs

Start with budget or advanced models for most use cases, they are faster and more cost-efficient. Move to expert models only when tasks require deeper reasoning, higher accuracy, or complex logic.

To reduce credit usage:

  • Use simpler models for repetitive or low-complexity tasks

  • Keep instructions clear to avoid unnecessary retries

  • Limit unnecessary tool calls or repeated executions

A good approach is to start simple, test performance, and only scale up when needed.

Managing Credits

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